{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "258ae7cc-a38d-439a-aef8-a739ede0d3ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in d:\\programdata\\anaconda3\\lib\\site-packages (1.26.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8132e1a6-f1e8-48ca-ac62-7db856f55fae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w= [2.13756953] b= [90.78228498]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "x=np.array([[100],[113],[90],[89],[60],[70],[50],[45],[55],[78]])\n",
    "y=np.array([[301],[324],[285],[296],[200],[260],[300],[120],[180],[245]])\n",
    "model=LinearRegression()\n",
    "model.fit(x,y)\n",
    "print(\"w=\",model.coef_[0],\"b=\",model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5577347e-4c74-4d80-82c1-7609b0f17cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "y2=model.predict(x)\n",
    "plt.xlabel('面积')\n",
    "plt.ylabel('售价')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.axis([40,125,100,400])\n",
    "plt.scatter(x,y,s=60,c='k',marker='o')\n",
    "plt.plot(x,y2,'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3b88a787-8ab7-486a-b2f3-a0092faef6d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w= [3.15636039] b= [0.04433569]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression,SGDRegressor\n",
    "x=np.array([[100],[113],[90],[89],[60],[70],[50],[45],[55],[78]])\n",
    "y=np.array([[301],[324],[285],[296],[200],[260],[300],[120],[180],[245]])\n",
    "model=SGDRegressor(loss='huber',max_iter=5000,random_state=42)\n",
    "model.fit(x,y.ravel())\n",
    "print(\"w=\",model.coef_,\"b=\",model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4818ebc8-7af1-4e46-82e7-2b7d3e3d0720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "y2=model.predict(x)\n",
    "plt.xlabel('面积')\n",
    "plt.ylabel('售价')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.axis([40,125,100,400])\n",
    "plt.scatter(x,y,s=60,c='k',marker='o')\n",
    "plt.plot(x,y2,'r-')\n",
    "plt.legend(['真实值','预测值'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68f26b59-6321-4c74-951b-b5783b91a85d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
